Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x2d31a909470>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x2d31a994cc0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
C:\Users\diogo\Anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
TensorFlow Version: 1.7.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    real_input = tf.placeholder(tf.float32, 
                                (None, image_width, image_height, image_channels),
                                name='real_input')
    z_input = tf.placeholder(tf.float32,
                             (None,z_dim),
                             name='z_input')
    lr = tf.placeholder(tf.float32, name='lr')
    
    return real_input, z_input, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.1
        conv1 = tf.layers.conv2d(images, 64, 5, 2, 'SAME')
        conv1 = tf.maximum(alpha*conv1, conv1)

        conv2 = tf.layers.conv2d(conv1, 128, 5, 2, 'SAME')
        conv2 = tf.layers.batch_normalization(conv2, training=True)
        conv2 = tf.maximum(alpha*conv2, conv2)

        conv3 = tf.layers.conv2d(conv2, 256, 5, 2, 'SAME')
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        conv3 = tf.maximum(alpha*conv3, conv3)

        fc   = tf.reshape(conv3, (-1, 4*4*256))
        fc = tf.layers.dense(fc, 1)

        out = tf.sigmoid(fc)
        
    return out, fc


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        g1 = tf.layers.dense(z, 2*2*512)
        g1 = tf.reshape(g1, (-1, 2, 2, 512))
        g1 = tf.layers.batch_normalization(g1, training=is_train)
        g1 = tf.maximum(alpha * g1, g1)

        g2 = tf.layers.conv2d_transpose(g1, 256, 5, 2, 'valid')
        g2 = tf.layers.batch_normalization(g2, training=is_train)
        g2 = tf.maximum(alpha * g2, g2)
    
        g3 = tf.layers.conv2d_transpose(g2, 128, 5, 2, 'same')
        g3 = tf.layers.batch_normalization(g3, training=is_train)
        g3 = tf.maximum(alpha * g3, g3)
    
        logits = tf.layers.conv2d_transpose(g3, out_channel_dim, 5, 2, 'same')
        out = tf.tanh(logits)
    
        return out

    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    generator_model = generator(input_z, out_channel_dim)
    dis_model_real, dis_logits_real = discriminator(input_real)
    dis_model_fake, dis_logits_fake = discriminator(generator_model, reuse=True)
    
    dis_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_logits_real, labels=tf.ones_like(dis_model_real))
    )
    
    dis_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_logits_fake, labels=tf.zeros_like(dis_model_fake))
    )
    
    generator_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=dis_logits_fake, labels=tf.ones_like(dis_model_fake))
    )
    
    discriminator_loss = dis_loss_real + dis_loss_fake

    return discriminator_loss, generator_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    variables = tf.trainable_variables()
    d_vars = [v for v in variables if v.name.startswith('discriminator')]
    g_vars = [v for v in variables if v.name.startswith('generator')]

    # Run optimizer
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train = tf.train.AdamOptimizer(learning_rate, beta1).minimize(d_loss, var_list=d_vars)
        g_train = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)

    return d_train, g_train    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, img_width, img_height, img_channels = data_shape
    
    input_real, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, img_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)

    step = 0
    print_every = 10
    show_every = 100
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                step += 1
                
                batch_images *= 2.0 # Rescale input to [-1, 1]
                
                batch_z = np.random.uniform(-1, 1, (batch_size, z_dim))

                _ = sess.run(d_opt, 
                             feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate}
                            )
                _ = sess.run(g_opt, 
                             feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate}
                            )
            
                if step % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch+1, epoch_count),
                          "Batch {}...".format(step),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if step % show_every == 0:
                    show_generator_output(sess, batch_size, input_z, img_channels, data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 2.3937... Generator Loss: 0.1264
Epoch 1/2... Batch 20... Discriminator Loss: 5.0852... Generator Loss: 0.0071
Epoch 1/2... Batch 30... Discriminator Loss: 3.0686... Generator Loss: 0.0571
Epoch 1/2... Batch 40... Discriminator Loss: 1.6498... Generator Loss: 0.2560
Epoch 1/2... Batch 50... Discriminator Loss: 0.9228... Generator Loss: 0.6181
Epoch 1/2... Batch 60... Discriminator Loss: 0.3674... Generator Loss: 4.6930
Epoch 1/2... Batch 70... Discriminator Loss: 0.9334... Generator Loss: 5.6865
Epoch 1/2... Batch 80... Discriminator Loss: 1.0609... Generator Loss: 0.5603
Epoch 1/2... Batch 90... Discriminator Loss: 0.9201... Generator Loss: 0.6762
Epoch 1/2... Batch 100... Discriminator Loss: 0.6791... Generator Loss: 0.8905
Epoch 1/2... Batch 110... Discriminator Loss: 0.5843... Generator Loss: 2.4750
Epoch 1/2... Batch 120... Discriminator Loss: 1.2275... Generator Loss: 3.2792
Epoch 1/2... Batch 130... Discriminator Loss: 0.6051... Generator Loss: 1.8649
Epoch 1/2... Batch 140... Discriminator Loss: 1.3635... Generator Loss: 1.1944
Epoch 1/2... Batch 150... Discriminator Loss: 0.7554... Generator Loss: 1.6111
Epoch 1/2... Batch 160... Discriminator Loss: 0.9195... Generator Loss: 2.3229
Epoch 1/2... Batch 170... Discriminator Loss: 0.9851... Generator Loss: 2.7066
Epoch 1/2... Batch 180... Discriminator Loss: 1.4281... Generator Loss: 0.4084
Epoch 1/2... Batch 190... Discriminator Loss: 1.7039... Generator Loss: 0.3020
Epoch 1/2... Batch 200... Discriminator Loss: 1.5074... Generator Loss: 0.3503
Epoch 1/2... Batch 210... Discriminator Loss: 1.4539... Generator Loss: 0.3969
Epoch 1/2... Batch 220... Discriminator Loss: 1.1566... Generator Loss: 0.5366
Epoch 1/2... Batch 230... Discriminator Loss: 1.2956... Generator Loss: 0.4227
Epoch 1/2... Batch 240... Discriminator Loss: 0.9539... Generator Loss: 0.8098
Epoch 1/2... Batch 250... Discriminator Loss: 1.5821... Generator Loss: 0.3037
Epoch 1/2... Batch 260... Discriminator Loss: 0.9210... Generator Loss: 0.8417
Epoch 1/2... Batch 270... Discriminator Loss: 1.3669... Generator Loss: 1.4291
Epoch 1/2... Batch 280... Discriminator Loss: 1.0538... Generator Loss: 1.6228
Epoch 1/2... Batch 290... Discriminator Loss: 1.0851... Generator Loss: 1.6774
Epoch 1/2... Batch 300... Discriminator Loss: 1.3050... Generator Loss: 0.4132
Epoch 1/2... Batch 310... Discriminator Loss: 0.8992... Generator Loss: 0.7386
Epoch 1/2... Batch 320... Discriminator Loss: 1.2299... Generator Loss: 0.5126
Epoch 1/2... Batch 330... Discriminator Loss: 1.3975... Generator Loss: 0.4002
Epoch 1/2... Batch 340... Discriminator Loss: 1.1275... Generator Loss: 0.5294
Epoch 1/2... Batch 350... Discriminator Loss: 0.9660... Generator Loss: 0.7943
Epoch 1/2... Batch 360... Discriminator Loss: 0.8478... Generator Loss: 0.9871
Epoch 1/2... Batch 370... Discriminator Loss: 1.4232... Generator Loss: 0.3592
Epoch 1/2... Batch 380... Discriminator Loss: 1.2345... Generator Loss: 0.4714
Epoch 1/2... Batch 390... Discriminator Loss: 0.8915... Generator Loss: 0.7675
Epoch 1/2... Batch 400... Discriminator Loss: 1.5327... Generator Loss: 0.2952
Epoch 1/2... Batch 410... Discriminator Loss: 1.2393... Generator Loss: 1.8728
Epoch 1/2... Batch 420... Discriminator Loss: 0.8764... Generator Loss: 1.8954
Epoch 1/2... Batch 430... Discriminator Loss: 0.9929... Generator Loss: 1.6605
Epoch 1/2... Batch 440... Discriminator Loss: 0.9185... Generator Loss: 0.9568
Epoch 1/2... Batch 450... Discriminator Loss: 0.8869... Generator Loss: 1.3088
Epoch 1/2... Batch 460... Discriminator Loss: 1.0119... Generator Loss: 0.9727
Epoch 1/2... Batch 470... Discriminator Loss: 1.2947... Generator Loss: 0.3833
Epoch 1/2... Batch 480... Discriminator Loss: 1.0943... Generator Loss: 0.5976
Epoch 1/2... Batch 490... Discriminator Loss: 1.8626... Generator Loss: 0.2078
Epoch 1/2... Batch 500... Discriminator Loss: 1.0323... Generator Loss: 0.5869
Epoch 1/2... Batch 510... Discriminator Loss: 1.2337... Generator Loss: 0.4626
Epoch 1/2... Batch 520... Discriminator Loss: 1.4777... Generator Loss: 0.3075
Epoch 1/2... Batch 530... Discriminator Loss: 1.0652... Generator Loss: 1.4728
Epoch 1/2... Batch 540... Discriminator Loss: 1.0614... Generator Loss: 1.3767
Epoch 1/2... Batch 550... Discriminator Loss: 0.9202... Generator Loss: 1.2209
Epoch 1/2... Batch 560... Discriminator Loss: 1.0746... Generator Loss: 1.8965
Epoch 1/2... Batch 570... Discriminator Loss: 0.9712... Generator Loss: 0.8321
Epoch 1/2... Batch 580... Discriminator Loss: 1.4738... Generator Loss: 0.3301
Epoch 1/2... Batch 590... Discriminator Loss: 1.0798... Generator Loss: 0.5597
Epoch 1/2... Batch 600... Discriminator Loss: 1.2087... Generator Loss: 1.5551
Epoch 1/2... Batch 610... Discriminator Loss: 0.9451... Generator Loss: 1.3335
Epoch 1/2... Batch 620... Discriminator Loss: 1.0252... Generator Loss: 1.2679
Epoch 1/2... Batch 630... Discriminator Loss: 0.9415... Generator Loss: 1.2501
Epoch 1/2... Batch 640... Discriminator Loss: 1.2498... Generator Loss: 0.4694
Epoch 1/2... Batch 650... Discriminator Loss: 1.4409... Generator Loss: 0.3646
Epoch 1/2... Batch 660... Discriminator Loss: 1.1341... Generator Loss: 0.6256
Epoch 1/2... Batch 670... Discriminator Loss: 1.3114... Generator Loss: 0.4563
Epoch 1/2... Batch 680... Discriminator Loss: 1.2763... Generator Loss: 2.0303
Epoch 1/2... Batch 690... Discriminator Loss: 1.4036... Generator Loss: 0.3699
Epoch 1/2... Batch 700... Discriminator Loss: 1.1512... Generator Loss: 0.5174
Epoch 1/2... Batch 710... Discriminator Loss: 1.5142... Generator Loss: 0.3066
Epoch 1/2... Batch 720... Discriminator Loss: 1.5197... Generator Loss: 0.3281
Epoch 1/2... Batch 730... Discriminator Loss: 0.9999... Generator Loss: 0.7039
Epoch 1/2... Batch 740... Discriminator Loss: 1.7290... Generator Loss: 0.2454
Epoch 1/2... Batch 750... Discriminator Loss: 1.3451... Generator Loss: 0.3862
Epoch 1/2... Batch 760... Discriminator Loss: 0.7432... Generator Loss: 1.0591
Epoch 1/2... Batch 770... Discriminator Loss: 0.9157... Generator Loss: 1.5049
Epoch 1/2... Batch 780... Discriminator Loss: 1.0094... Generator Loss: 0.8582
Epoch 1/2... Batch 790... Discriminator Loss: 1.0807... Generator Loss: 0.7454
Epoch 1/2... Batch 800... Discriminator Loss: 1.2131... Generator Loss: 1.3079
Epoch 1/2... Batch 810... Discriminator Loss: 0.8818... Generator Loss: 1.0560
Epoch 1/2... Batch 820... Discriminator Loss: 1.4798... Generator Loss: 0.3231
Epoch 1/2... Batch 830... Discriminator Loss: 1.5988... Generator Loss: 0.3099
Epoch 1/2... Batch 840... Discriminator Loss: 0.8951... Generator Loss: 0.9544
Epoch 1/2... Batch 850... Discriminator Loss: 0.8917... Generator Loss: 0.9754
Epoch 1/2... Batch 860... Discriminator Loss: 0.9576... Generator Loss: 1.2432
Epoch 1/2... Batch 870... Discriminator Loss: 1.2886... Generator Loss: 0.4233
Epoch 1/2... Batch 880... Discriminator Loss: 1.5517... Generator Loss: 0.3006
Epoch 1/2... Batch 890... Discriminator Loss: 1.0258... Generator Loss: 0.8150
Epoch 1/2... Batch 900... Discriminator Loss: 0.9562... Generator Loss: 0.8891
Epoch 1/2... Batch 910... Discriminator Loss: 0.9626... Generator Loss: 1.8835
Epoch 1/2... Batch 920... Discriminator Loss: 1.1061... Generator Loss: 0.7621
Epoch 1/2... Batch 930... Discriminator Loss: 1.4987... Generator Loss: 0.3492
Epoch 1/2... Batch 940... Discriminator Loss: 1.0619... Generator Loss: 0.6869
Epoch 1/2... Batch 950... Discriminator Loss: 0.8098... Generator Loss: 1.4637
Epoch 1/2... Batch 960... Discriminator Loss: 1.3971... Generator Loss: 1.8989
Epoch 1/2... Batch 970... Discriminator Loss: 1.1845... Generator Loss: 0.5091
Epoch 1/2... Batch 980... Discriminator Loss: 0.8709... Generator Loss: 1.4068
Epoch 1/2... Batch 990... Discriminator Loss: 1.0074... Generator Loss: 1.1104
Epoch 1/2... Batch 1000... Discriminator Loss: 1.2229... Generator Loss: 0.4512
Epoch 1/2... Batch 1010... Discriminator Loss: 0.8413... Generator Loss: 0.9431
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Epoch 2/2... Batch 3630... Discriminator Loss: 0.6169... Generator Loss: 1.0598
Epoch 2/2... Batch 3640... Discriminator Loss: 1.5794... Generator Loss: 0.3156
Epoch 2/2... Batch 3650... Discriminator Loss: 0.6342... Generator Loss: 0.9345
Epoch 2/2... Batch 3660... Discriminator Loss: 0.3027... Generator Loss: 1.8469
Epoch 2/2... Batch 3670... Discriminator Loss: 0.4758... Generator Loss: 1.7491
Epoch 2/2... Batch 3680... Discriminator Loss: 0.5041... Generator Loss: 1.4875
Epoch 2/2... Batch 3690... Discriminator Loss: 0.4254... Generator Loss: 1.4345
Epoch 2/2... Batch 3700... Discriminator Loss: 1.2879... Generator Loss: 2.9420
Epoch 2/2... Batch 3710... Discriminator Loss: 0.8396... Generator Loss: 2.6735
Epoch 2/2... Batch 3720... Discriminator Loss: 0.6526... Generator Loss: 2.7267
Epoch 2/2... Batch 3730... Discriminator Loss: 0.5483... Generator Loss: 1.2014
Epoch 2/2... Batch 3740... Discriminator Loss: 0.7842... Generator Loss: 0.9197
Epoch 2/2... Batch 3750... Discriminator Loss: 0.5577... Generator Loss: 1.1672

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 0.9293... Generator Loss: 4.6178
Epoch 1/1... Batch 20... Discriminator Loss: 1.3472... Generator Loss: 6.4690
Epoch 1/1... Batch 30... Discriminator Loss: 0.4701... Generator Loss: 1.4569
Epoch 1/1... Batch 40... Discriminator Loss: 1.3980... Generator Loss: 6.2590
Epoch 1/1... Batch 50... Discriminator Loss: 3.4545... Generator Loss: 0.0445
Epoch 1/1... Batch 60... Discriminator Loss: 1.1009... Generator Loss: 0.6570
Epoch 1/1... Batch 70... Discriminator Loss: 0.8478... Generator Loss: 7.0559
Epoch 1/1... Batch 80... Discriminator Loss: 0.5560... Generator Loss: 4.3594
Epoch 1/1... Batch 90... Discriminator Loss: 0.6818... Generator Loss: 1.2792
Epoch 1/1... Batch 100... Discriminator Loss: 1.1214... Generator Loss: 0.5480
Epoch 1/1... Batch 110... Discriminator Loss: 1.1927... Generator Loss: 0.4901
Epoch 1/1... Batch 120... Discriminator Loss: 2.0369... Generator Loss: 0.2242
Epoch 1/1... Batch 130... Discriminator Loss: 1.2792... Generator Loss: 0.6574
Epoch 1/1... Batch 140... Discriminator Loss: 1.3093... Generator Loss: 1.7518
Epoch 1/1... Batch 150... Discriminator Loss: 1.0852... Generator Loss: 1.7641
Epoch 1/1... Batch 160... Discriminator Loss: 2.0522... Generator Loss: 0.5253
Epoch 1/1... Batch 170... Discriminator Loss: 1.5753... Generator Loss: 0.3811
Epoch 1/1... Batch 180... Discriminator Loss: 1.1223... Generator Loss: 0.7062
Epoch 1/1... Batch 190... Discriminator Loss: 1.2497... Generator Loss: 0.6594
Epoch 1/1... Batch 200... Discriminator Loss: 1.3202... Generator Loss: 0.4685
Epoch 1/1... Batch 210... Discriminator Loss: 1.5817... Generator Loss: 0.4117
Epoch 1/1... Batch 220... Discriminator Loss: 1.2982... Generator Loss: 1.0016
Epoch 1/1... Batch 230... Discriminator Loss: 1.2579... Generator Loss: 0.5509
Epoch 1/1... Batch 240... Discriminator Loss: 1.2772... Generator Loss: 2.0282
Epoch 1/1... Batch 250... Discriminator Loss: 0.8452... Generator Loss: 0.9409
Epoch 1/1... Batch 260... Discriminator Loss: 1.7469... Generator Loss: 0.3216
Epoch 1/1... Batch 270... Discriminator Loss: 1.7351... Generator Loss: 0.3034
Epoch 1/1... Batch 280... Discriminator Loss: 1.2725... Generator Loss: 0.5359
Epoch 1/1... Batch 290... Discriminator Loss: 1.9038... Generator Loss: 0.2601
Epoch 1/1... Batch 300... Discriminator Loss: 1.0538... Generator Loss: 1.3314
Epoch 1/1... Batch 310... Discriminator Loss: 1.8741... Generator Loss: 0.2438
Epoch 1/1... Batch 320... Discriminator Loss: 1.5379... Generator Loss: 0.3046
Epoch 1/1... Batch 330... Discriminator Loss: 1.2692... Generator Loss: 1.0037
Epoch 1/1... Batch 340... Discriminator Loss: 1.5191... Generator Loss: 0.3571
Epoch 1/1... Batch 350... Discriminator Loss: 1.3639... Generator Loss: 0.4862
Epoch 1/1... Batch 360... Discriminator Loss: 1.4046... Generator Loss: 0.4170
Epoch 1/1... Batch 370... Discriminator Loss: 1.1290... Generator Loss: 0.6005
Epoch 1/1... Batch 380... Discriminator Loss: 0.9580... Generator Loss: 2.3115
Epoch 1/1... Batch 390... Discriminator Loss: 1.2442... Generator Loss: 1.3643
Epoch 1/1... Batch 400... Discriminator Loss: 0.9371... Generator Loss: 1.5374
Epoch 1/1... Batch 410... Discriminator Loss: 0.7748... Generator Loss: 1.4060
Epoch 1/1... Batch 420... Discriminator Loss: 0.9980... Generator Loss: 2.1361
Epoch 1/1... Batch 430... Discriminator Loss: 0.9691... Generator Loss: 1.5636
Epoch 1/1... Batch 440... Discriminator Loss: 0.6672... Generator Loss: 1.4566
Epoch 1/1... Batch 450... Discriminator Loss: 0.7830... Generator Loss: 2.1104
Epoch 1/1... Batch 460... Discriminator Loss: 0.6820... Generator Loss: 1.8129
Epoch 1/1... Batch 470... Discriminator Loss: 1.2348... Generator Loss: 1.2722
Epoch 1/1... Batch 480... Discriminator Loss: 1.9680... Generator Loss: 0.2081
Epoch 1/1... Batch 490... Discriminator Loss: 1.9481... Generator Loss: 0.2079
Epoch 1/1... Batch 500... Discriminator Loss: 1.1937... Generator Loss: 0.5366
Epoch 1/1... Batch 510... Discriminator Loss: 0.9786... Generator Loss: 0.6506
Epoch 1/1... Batch 520... Discriminator Loss: 0.7981... Generator Loss: 0.8508
Epoch 1/1... Batch 530... Discriminator Loss: 0.8115... Generator Loss: 1.3778
Epoch 1/1... Batch 540... Discriminator Loss: 1.1664... Generator Loss: 0.6215
Epoch 1/1... Batch 550... Discriminator Loss: 1.1248... Generator Loss: 1.5188
Epoch 1/1... Batch 560... Discriminator Loss: 1.2114... Generator Loss: 1.4664
Epoch 1/1... Batch 570... Discriminator Loss: 0.7170... Generator Loss: 1.9029
Epoch 1/1... Batch 580... Discriminator Loss: 0.6382... Generator Loss: 1.1426
Epoch 1/1... Batch 590... Discriminator Loss: 0.7241... Generator Loss: 0.9276
Epoch 1/1... Batch 600... Discriminator Loss: 0.8000... Generator Loss: 0.8583
Epoch 1/1... Batch 610... Discriminator Loss: 0.7334... Generator Loss: 3.1229
Epoch 1/1... Batch 620... Discriminator Loss: 1.2471... Generator Loss: 2.6443
Epoch 1/1... Batch 630... Discriminator Loss: 0.8311... Generator Loss: 1.7215
Epoch 1/1... Batch 640... Discriminator Loss: 0.8244... Generator Loss: 0.8366
Epoch 1/1... Batch 650... Discriminator Loss: 1.4196... Generator Loss: 0.3816
Epoch 1/1... Batch 660... Discriminator Loss: 1.6584... Generator Loss: 0.3014
Epoch 1/1... Batch 670... Discriminator Loss: 0.7246... Generator Loss: 0.9517
Epoch 1/1... Batch 680... Discriminator Loss: 1.9880... Generator Loss: 0.1939
Epoch 1/1... Batch 690... Discriminator Loss: 1.9988... Generator Loss: 0.1846
Epoch 1/1... Batch 700... Discriminator Loss: 0.9264... Generator Loss: 0.9363
Epoch 1/1... Batch 710... Discriminator Loss: 1.2120... Generator Loss: 1.7180
Epoch 1/1... Batch 720... Discriminator Loss: 0.7567... Generator Loss: 1.8830
Epoch 1/1... Batch 730... Discriminator Loss: 1.0468... Generator Loss: 0.5381
Epoch 1/1... Batch 740... Discriminator Loss: 1.5002... Generator Loss: 0.3574
Epoch 1/1... Batch 750... Discriminator Loss: 1.6204... Generator Loss: 0.3260
Epoch 1/1... Batch 760... Discriminator Loss: 1.7188... Generator Loss: 0.2821
Epoch 1/1... Batch 770... Discriminator Loss: 1.3598... Generator Loss: 0.4311
Epoch 1/1... Batch 780... Discriminator Loss: 1.1277... Generator Loss: 0.4991
Epoch 1/1... Batch 790... Discriminator Loss: 1.3731... Generator Loss: 0.3948
Epoch 1/1... Batch 800... Discriminator Loss: 0.9639... Generator Loss: 0.6625
Epoch 1/1... Batch 810... Discriminator Loss: 0.7835... Generator Loss: 0.8961
Epoch 1/1... Batch 820... Discriminator Loss: 1.5526... Generator Loss: 0.3371
Epoch 1/1... Batch 830... Discriminator Loss: 2.0865... Generator Loss: 0.1987
Epoch 1/1... Batch 840... Discriminator Loss: 0.9100... Generator Loss: 0.7230
Epoch 1/1... Batch 850... Discriminator Loss: 1.2878... Generator Loss: 0.3845
Epoch 1/1... Batch 860... Discriminator Loss: 2.2869... Generator Loss: 0.1300
Epoch 1/1... Batch 870... Discriminator Loss: 1.2734... Generator Loss: 0.5061
Epoch 1/1... Batch 880... Discriminator Loss: 1.0578... Generator Loss: 0.6190
Epoch 1/1... Batch 890... Discriminator Loss: 1.2635... Generator Loss: 0.4362
Epoch 1/1... Batch 900... Discriminator Loss: 1.9000... Generator Loss: 0.2129
Epoch 1/1... Batch 910... Discriminator Loss: 1.7149... Generator Loss: 0.3093
Epoch 1/1... Batch 920... Discriminator Loss: 1.0146... Generator Loss: 1.3915
Epoch 1/1... Batch 930... Discriminator Loss: 0.6421... Generator Loss: 1.7266
Epoch 1/1... Batch 940... Discriminator Loss: 1.3039... Generator Loss: 1.2176
Epoch 1/1... Batch 950... Discriminator Loss: 1.2399... Generator Loss: 1.6353
Epoch 1/1... Batch 960... Discriminator Loss: 0.8751... Generator Loss: 1.5914
Epoch 1/1... Batch 970... Discriminator Loss: 0.9181... Generator Loss: 3.1697
Epoch 1/1... Batch 980... Discriminator Loss: 1.2013... Generator Loss: 2.0427
Epoch 1/1... Batch 990... Discriminator Loss: 0.7307... Generator Loss: 1.6139
Epoch 1/1... Batch 1000... Discriminator Loss: 0.6657... Generator Loss: 1.9088
Epoch 1/1... Batch 1010... Discriminator Loss: 1.2239... Generator Loss: 3.8064
Epoch 1/1... Batch 1020... Discriminator Loss: 0.6440... Generator Loss: 2.1346
Epoch 1/1... Batch 1030... Discriminator Loss: 0.9821... Generator Loss: 2.1438
Epoch 1/1... Batch 1040... Discriminator Loss: 0.7546... Generator Loss: 3.8386
Epoch 1/1... Batch 1050... Discriminator Loss: 0.9884... Generator Loss: 4.3220
Epoch 1/1... Batch 1060... Discriminator Loss: 0.6572... Generator Loss: 2.1619
Epoch 1/1... Batch 1070... Discriminator Loss: 0.6843... Generator Loss: 2.4549
Epoch 1/1... Batch 1080... Discriminator Loss: 0.7017... Generator Loss: 2.3019
Epoch 1/1... Batch 1090... Discriminator Loss: 1.1256... Generator Loss: 2.4012
Epoch 1/1... Batch 1100... Discriminator Loss: 0.6730... Generator Loss: 1.0385
Epoch 1/1... Batch 1110... Discriminator Loss: 1.6243... Generator Loss: 0.2690
Epoch 1/1... Batch 1120... Discriminator Loss: 1.6642... Generator Loss: 0.2739
Epoch 1/1... Batch 1130... Discriminator Loss: 0.8979... Generator Loss: 0.7340
Epoch 1/1... Batch 1140... Discriminator Loss: 1.9073... Generator Loss: 0.2089
Epoch 1/1... Batch 1150... Discriminator Loss: 1.4565... Generator Loss: 0.3835
Epoch 1/1... Batch 1160... Discriminator Loss: 1.4403... Generator Loss: 0.3358
Epoch 1/1... Batch 1170... Discriminator Loss: 1.4970... Generator Loss: 0.3205
Epoch 1/1... Batch 1180... Discriminator Loss: 0.5409... Generator Loss: 1.5713
Epoch 1/1... Batch 1190... Discriminator Loss: 1.3169... Generator Loss: 0.4798
Epoch 1/1... Batch 1200... Discriminator Loss: 1.2704... Generator Loss: 0.4192
Epoch 1/1... Batch 1210... Discriminator Loss: 2.2393... Generator Loss: 0.1406
Epoch 1/1... Batch 1220... Discriminator Loss: 1.0680... Generator Loss: 2.2074
Epoch 1/1... Batch 1230... Discriminator Loss: 0.7462... Generator Loss: 1.7602
Epoch 1/1... Batch 1240... Discriminator Loss: 1.3110... Generator Loss: 1.4601
Epoch 1/1... Batch 1250... Discriminator Loss: 0.8373... Generator Loss: 2.8978
Epoch 1/1... Batch 1260... Discriminator Loss: 1.0001... Generator Loss: 1.3378
Epoch 1/1... Batch 1270... Discriminator Loss: 0.6362... Generator Loss: 1.4565
Epoch 1/1... Batch 1280... Discriminator Loss: 1.1351... Generator Loss: 1.3223
Epoch 1/1... Batch 1290... Discriminator Loss: 0.9990... Generator Loss: 0.9353
Epoch 1/1... Batch 1300... Discriminator Loss: 1.4517... Generator Loss: 0.3524
Epoch 1/1... Batch 1310... Discriminator Loss: 1.0221... Generator Loss: 0.5934
Epoch 1/1... Batch 1320... Discriminator Loss: 1.3652... Generator Loss: 0.3589
Epoch 1/1... Batch 1330... Discriminator Loss: 1.1499... Generator Loss: 0.5060
Epoch 1/1... Batch 1340... Discriminator Loss: 0.8650... Generator Loss: 3.6173
Epoch 1/1... Batch 1350... Discriminator Loss: 0.2325... Generator Loss: 2.5502
Epoch 1/1... Batch 1360... Discriminator Loss: 0.3396... Generator Loss: 3.4657
Epoch 1/1... Batch 1370... Discriminator Loss: 0.6410... Generator Loss: 1.6947
Epoch 1/1... Batch 1380... Discriminator Loss: 1.1415... Generator Loss: 1.6332
Epoch 1/1... Batch 1390... Discriminator Loss: 0.5057... Generator Loss: 1.3675
Epoch 1/1... Batch 1400... Discriminator Loss: 1.2021... Generator Loss: 1.0736
Epoch 1/1... Batch 1410... Discriminator Loss: 1.4364... Generator Loss: 0.3570
Epoch 1/1... Batch 1420... Discriminator Loss: 1.5375... Generator Loss: 0.3280
Epoch 1/1... Batch 1430... Discriminator Loss: 1.5768... Generator Loss: 0.2958
Epoch 1/1... Batch 1440... Discriminator Loss: 1.4220... Generator Loss: 0.3318
Epoch 1/1... Batch 1450... Discriminator Loss: 0.7300... Generator Loss: 2.0419
Epoch 1/1... Batch 1460... Discriminator Loss: 1.9396... Generator Loss: 0.1769
Epoch 1/1... Batch 1470... Discriminator Loss: 0.9321... Generator Loss: 0.6702
Epoch 1/1... Batch 1480... Discriminator Loss: 1.7580... Generator Loss: 2.1855
Epoch 1/1... Batch 1490... Discriminator Loss: 0.6207... Generator Loss: 2.0251
Epoch 1/1... Batch 1500... Discriminator Loss: 0.7807... Generator Loss: 0.8359
Epoch 1/1... Batch 1510... Discriminator Loss: 1.2266... Generator Loss: 0.5486
Epoch 1/1... Batch 1520... Discriminator Loss: 1.0986... Generator Loss: 0.5068
Epoch 1/1... Batch 1530... Discriminator Loss: 1.1965... Generator Loss: 0.4818
Epoch 1/1... Batch 1540... Discriminator Loss: 1.8545... Generator Loss: 0.2094
Epoch 1/1... Batch 1550... Discriminator Loss: 1.0998... Generator Loss: 0.5378
Epoch 1/1... Batch 1560... Discriminator Loss: 0.9474... Generator Loss: 1.1050
Epoch 1/1... Batch 1570... Discriminator Loss: 0.9582... Generator Loss: 1.9153
Epoch 1/1... Batch 1580... Discriminator Loss: 0.5919... Generator Loss: 1.6849
Epoch 1/1... Batch 1590... Discriminator Loss: 1.1318... Generator Loss: 0.6355
Epoch 1/1... Batch 1600... Discriminator Loss: 1.2115... Generator Loss: 0.5101
Epoch 1/1... Batch 1610... Discriminator Loss: 1.2875... Generator Loss: 0.4277
Epoch 1/1... Batch 1620... Discriminator Loss: 1.1184... Generator Loss: 2.1760
Epoch 1/1... Batch 1630... Discriminator Loss: 0.6839... Generator Loss: 2.0794
Epoch 1/1... Batch 1640... Discriminator Loss: 1.8650... Generator Loss: 2.1137
Epoch 1/1... Batch 1650... Discriminator Loss: 1.1767... Generator Loss: 1.2850
Epoch 1/1... Batch 1660... Discriminator Loss: 1.4925... Generator Loss: 0.3132
Epoch 1/1... Batch 1670... Discriminator Loss: 0.8926... Generator Loss: 2.4069
Epoch 1/1... Batch 1680... Discriminator Loss: 1.3355... Generator Loss: 0.4196
Epoch 1/1... Batch 1690... Discriminator Loss: 1.0342... Generator Loss: 0.6349
Epoch 1/1... Batch 1700... Discriminator Loss: 1.4028... Generator Loss: 0.4039
Epoch 1/1... Batch 1710... Discriminator Loss: 0.7102... Generator Loss: 1.2563
Epoch 1/1... Batch 1720... Discriminator Loss: 1.1902... Generator Loss: 0.7919
Epoch 1/1... Batch 1730... Discriminator Loss: 0.8612... Generator Loss: 1.9504
Epoch 1/1... Batch 1740... Discriminator Loss: 0.7286... Generator Loss: 1.4550
Epoch 1/1... Batch 1750... Discriminator Loss: 1.3589... Generator Loss: 0.4231
Epoch 1/1... Batch 1760... Discriminator Loss: 1.1390... Generator Loss: 0.5078
Epoch 1/1... Batch 1770... Discriminator Loss: 0.6007... Generator Loss: 1.1087
Epoch 1/1... Batch 1780... Discriminator Loss: 0.8828... Generator Loss: 1.0289
Epoch 1/1... Batch 1790... Discriminator Loss: 1.1472... Generator Loss: 0.7805
Epoch 1/1... Batch 1800... Discriminator Loss: 0.9898... Generator Loss: 0.9785
Epoch 1/1... Batch 1810... Discriminator Loss: 1.0634... Generator Loss: 0.5513
Epoch 1/1... Batch 1820... Discriminator Loss: 1.5433... Generator Loss: 0.2918
Epoch 1/1... Batch 1830... Discriminator Loss: 0.6018... Generator Loss: 1.0767
Epoch 1/1... Batch 1840... Discriminator Loss: 1.6001... Generator Loss: 0.3139
Epoch 1/1... Batch 1850... Discriminator Loss: 1.0796... Generator Loss: 1.9808
Epoch 1/1... Batch 1860... Discriminator Loss: 0.9702... Generator Loss: 0.7678
Epoch 1/1... Batch 1870... Discriminator Loss: 0.5047... Generator Loss: 1.2954
Epoch 1/1... Batch 1880... Discriminator Loss: 0.4797... Generator Loss: 2.0780
Epoch 1/1... Batch 1890... Discriminator Loss: 1.6994... Generator Loss: 0.2219
Epoch 1/1... Batch 1900... Discriminator Loss: 0.6629... Generator Loss: 1.2097
Epoch 1/1... Batch 1910... Discriminator Loss: 1.1555... Generator Loss: 1.4278
Epoch 1/1... Batch 1920... Discriminator Loss: 1.7851... Generator Loss: 0.2241
Epoch 1/1... Batch 1930... Discriminator Loss: 1.2170... Generator Loss: 1.0144
Epoch 1/1... Batch 1940... Discriminator Loss: 1.0530... Generator Loss: 0.6483
Epoch 1/1... Batch 1950... Discriminator Loss: 1.6680... Generator Loss: 0.2426
Epoch 1/1... Batch 1960... Discriminator Loss: 1.6923... Generator Loss: 0.2518
Epoch 1/1... Batch 1970... Discriminator Loss: 0.8100... Generator Loss: 3.3433
Epoch 1/1... Batch 1980... Discriminator Loss: 1.3545... Generator Loss: 0.3608
Epoch 1/1... Batch 1990... Discriminator Loss: 1.4415... Generator Loss: 0.3121
Epoch 1/1... Batch 2000... Discriminator Loss: 1.1111... Generator Loss: 0.5243
Epoch 1/1... Batch 2010... Discriminator Loss: 1.7082... Generator Loss: 0.2554
Epoch 1/1... Batch 2020... Discriminator Loss: 0.5937... Generator Loss: 1.8893
Epoch 1/1... Batch 2030... Discriminator Loss: 1.0583... Generator Loss: 0.5215
Epoch 1/1... Batch 2040... Discriminator Loss: 0.3589... Generator Loss: 2.3755
Epoch 1/1... Batch 2050... Discriminator Loss: 1.3542... Generator Loss: 0.4520
Epoch 1/1... Batch 2060... Discriminator Loss: 1.1163... Generator Loss: 0.6133
Epoch 1/1... Batch 2070... Discriminator Loss: 1.0757... Generator Loss: 1.1587
Epoch 1/1... Batch 2080... Discriminator Loss: 1.4007... Generator Loss: 1.5334
Epoch 1/1... Batch 2090... Discriminator Loss: 1.4229... Generator Loss: 0.3319
Epoch 1/1... Batch 2100... Discriminator Loss: 0.9201... Generator Loss: 0.7214
Epoch 1/1... Batch 2110... Discriminator Loss: 1.1153... Generator Loss: 0.5626
Epoch 1/1... Batch 2120... Discriminator Loss: 0.8889... Generator Loss: 0.6625
Epoch 1/1... Batch 2130... Discriminator Loss: 1.0927... Generator Loss: 0.6686
Epoch 1/1... Batch 2140... Discriminator Loss: 1.1966... Generator Loss: 0.5847
Epoch 1/1... Batch 2150... Discriminator Loss: 0.8676... Generator Loss: 1.1218
Epoch 1/1... Batch 2160... Discriminator Loss: 1.8375... Generator Loss: 0.2371
Epoch 1/1... Batch 2170... Discriminator Loss: 0.6602... Generator Loss: 2.1635
Epoch 1/1... Batch 2180... Discriminator Loss: 0.7367... Generator Loss: 1.5460
Epoch 1/1... Batch 2190... Discriminator Loss: 1.3933... Generator Loss: 1.1123
Epoch 1/1... Batch 2200... Discriminator Loss: 1.0245... Generator Loss: 0.6010
Epoch 1/1... Batch 2210... Discriminator Loss: 1.0288... Generator Loss: 0.7865
Epoch 1/1... Batch 2220... Discriminator Loss: 2.1931... Generator Loss: 0.1420
Epoch 1/1... Batch 2230... Discriminator Loss: 1.5920... Generator Loss: 0.3128
Epoch 1/1... Batch 2240... Discriminator Loss: 1.2821... Generator Loss: 0.4852
Epoch 1/1... Batch 2250... Discriminator Loss: 1.4529... Generator Loss: 0.3536
Epoch 1/1... Batch 2260... Discriminator Loss: 2.0074... Generator Loss: 0.1939
Epoch 1/1... Batch 2270... Discriminator Loss: 1.3090... Generator Loss: 0.4195
Epoch 1/1... Batch 2280... Discriminator Loss: 1.0639... Generator Loss: 0.5873
Epoch 1/1... Batch 2290... Discriminator Loss: 1.3371... Generator Loss: 0.4193
Epoch 1/1... Batch 2300... Discriminator Loss: 1.2894... Generator Loss: 0.4135
Epoch 1/1... Batch 2310... Discriminator Loss: 1.2547... Generator Loss: 1.4496
Epoch 1/1... Batch 2320... Discriminator Loss: 0.9990... Generator Loss: 2.3254
Epoch 1/1... Batch 2330... Discriminator Loss: 1.4676... Generator Loss: 0.3646
Epoch 1/1... Batch 2340... Discriminator Loss: 0.8519... Generator Loss: 0.7183
Epoch 1/1... Batch 2350... Discriminator Loss: 1.9923... Generator Loss: 0.1751
Epoch 1/1... Batch 2360... Discriminator Loss: 1.3772... Generator Loss: 0.3512
Epoch 1/1... Batch 2370... Discriminator Loss: 1.0913... Generator Loss: 1.9928
Epoch 1/1... Batch 2380... Discriminator Loss: 1.3219... Generator Loss: 0.4867
Epoch 1/1... Batch 2390... Discriminator Loss: 1.1906... Generator Loss: 0.5554
Epoch 1/1... Batch 2400... Discriminator Loss: 0.8587... Generator Loss: 0.7477
Epoch 1/1... Batch 2410... Discriminator Loss: 1.4359... Generator Loss: 0.3327
Epoch 1/1... Batch 2420... Discriminator Loss: 0.6674... Generator Loss: 1.5608
Epoch 1/1... Batch 2430... Discriminator Loss: 1.7055... Generator Loss: 1.9072
Epoch 1/1... Batch 2440... Discriminator Loss: 1.6736... Generator Loss: 0.3072
Epoch 1/1... Batch 2450... Discriminator Loss: 0.8435... Generator Loss: 1.0340
Epoch 1/1... Batch 2460... Discriminator Loss: 1.0746... Generator Loss: 0.7738
Epoch 1/1... Batch 2470... Discriminator Loss: 1.1212... Generator Loss: 0.5389
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Epoch 1/1... Batch 5310... Discriminator Loss: 0.8654... Generator Loss: 0.9926
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Epoch 1/1... Batch 5330... Discriminator Loss: 1.6098... Generator Loss: 0.2777
Epoch 1/1... Batch 5340... Discriminator Loss: 1.6393... Generator Loss: 0.2843
Epoch 1/1... Batch 5350... Discriminator Loss: 1.6781... Generator Loss: 0.2736
Epoch 1/1... Batch 5360... Discriminator Loss: 1.2704... Generator Loss: 0.4411
Epoch 1/1... Batch 5370... Discriminator Loss: 0.8559... Generator Loss: 1.2739
Epoch 1/1... Batch 5380... Discriminator Loss: 1.3565... Generator Loss: 0.4559
Epoch 1/1... Batch 5390... Discriminator Loss: 0.8494... Generator Loss: 1.7090
Epoch 1/1... Batch 5400... Discriminator Loss: 0.8168... Generator Loss: 0.9921
Epoch 1/1... Batch 5410... Discriminator Loss: 0.9758... Generator Loss: 0.8913
Epoch 1/1... Batch 5420... Discriminator Loss: 1.0611... Generator Loss: 0.5318
Epoch 1/1... Batch 5430... Discriminator Loss: 1.4775... Generator Loss: 0.3215
Epoch 1/1... Batch 5440... Discriminator Loss: 1.8167... Generator Loss: 0.2386
Epoch 1/1... Batch 5450... Discriminator Loss: 1.1530... Generator Loss: 0.5573
Epoch 1/1... Batch 5460... Discriminator Loss: 1.3037... Generator Loss: 0.6164
Epoch 1/1... Batch 5470... Discriminator Loss: 1.7729... Generator Loss: 1.9592
Epoch 1/1... Batch 5480... Discriminator Loss: 1.7233... Generator Loss: 0.2735
Epoch 1/1... Batch 5490... Discriminator Loss: 0.8807... Generator Loss: 2.1572
Epoch 1/1... Batch 5500... Discriminator Loss: 1.3282... Generator Loss: 2.0101
Epoch 1/1... Batch 5510... Discriminator Loss: 1.2439... Generator Loss: 0.4687
Epoch 1/1... Batch 5520... Discriminator Loss: 1.6234... Generator Loss: 0.3342
Epoch 1/1... Batch 5530... Discriminator Loss: 1.6322... Generator Loss: 0.2680
Epoch 1/1... Batch 5540... Discriminator Loss: 1.1542... Generator Loss: 0.7493
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Epoch 1/1... Batch 5560... Discriminator Loss: 1.2811... Generator Loss: 0.4749
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Epoch 1/1... Batch 5580... Discriminator Loss: 1.0646... Generator Loss: 0.9226
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Epoch 1/1... Batch 5600... Discriminator Loss: 1.0200... Generator Loss: 0.5772
Epoch 1/1... Batch 5610... Discriminator Loss: 1.8740... Generator Loss: 0.2040
Epoch 1/1... Batch 5620... Discriminator Loss: 1.4627... Generator Loss: 0.3580
Epoch 1/1... Batch 5630... Discriminator Loss: 1.4310... Generator Loss: 1.9678
Epoch 1/1... Batch 5640... Discriminator Loss: 1.0796... Generator Loss: 0.6172
Epoch 1/1... Batch 5650... Discriminator Loss: 0.7917... Generator Loss: 0.8555
Epoch 1/1... Batch 5660... Discriminator Loss: 1.3862... Generator Loss: 0.3606
Epoch 1/1... Batch 5670... Discriminator Loss: 1.0096... Generator Loss: 2.1855
Epoch 1/1... Batch 5680... Discriminator Loss: 1.0422... Generator Loss: 1.0493
Epoch 1/1... Batch 5690... Discriminator Loss: 1.1959... Generator Loss: 0.5045
Epoch 1/1... Batch 5700... Discriminator Loss: 0.8911... Generator Loss: 0.6797
Epoch 1/1... Batch 5710... Discriminator Loss: 1.3125... Generator Loss: 0.3886
Epoch 1/1... Batch 5720... Discriminator Loss: 0.8622... Generator Loss: 0.8218
Epoch 1/1... Batch 5730... Discriminator Loss: 1.5839... Generator Loss: 0.2686
Epoch 1/1... Batch 5740... Discriminator Loss: 2.2482... Generator Loss: 0.1427
Epoch 1/1... Batch 5750... Discriminator Loss: 1.2703... Generator Loss: 0.5673
Epoch 1/1... Batch 5760... Discriminator Loss: 0.8816... Generator Loss: 1.0579
Epoch 1/1... Batch 5770... Discriminator Loss: 1.1173... Generator Loss: 1.3676
Epoch 1/1... Batch 5780... Discriminator Loss: 0.8710... Generator Loss: 1.4275
Epoch 1/1... Batch 5790... Discriminator Loss: 1.0220... Generator Loss: 0.6540
Epoch 1/1... Batch 5800... Discriminator Loss: 1.2979... Generator Loss: 0.4666
Epoch 1/1... Batch 5810... Discriminator Loss: 0.8900... Generator Loss: 1.1129
Epoch 1/1... Batch 5820... Discriminator Loss: 1.0968... Generator Loss: 0.7440
Epoch 1/1... Batch 5830... Discriminator Loss: 2.1561... Generator Loss: 0.1520
Epoch 1/1... Batch 5840... Discriminator Loss: 1.2402... Generator Loss: 0.5451
Epoch 1/1... Batch 5850... Discriminator Loss: 1.1321... Generator Loss: 0.5620
Epoch 1/1... Batch 5860... Discriminator Loss: 1.2480... Generator Loss: 0.4396
Epoch 1/1... Batch 5870... Discriminator Loss: 1.1833... Generator Loss: 0.5103
Epoch 1/1... Batch 5880... Discriminator Loss: 1.4830... Generator Loss: 0.3293
Epoch 1/1... Batch 5890... Discriminator Loss: 0.9875... Generator Loss: 0.6928
Epoch 1/1... Batch 5900... Discriminator Loss: 1.3094... Generator Loss: 0.3869
Epoch 1/1... Batch 5910... Discriminator Loss: 1.2444... Generator Loss: 0.4820
Epoch 1/1... Batch 5920... Discriminator Loss: 0.7028... Generator Loss: 0.9929
Epoch 1/1... Batch 5930... Discriminator Loss: 0.8915... Generator Loss: 0.6938
Epoch 1/1... Batch 5940... Discriminator Loss: 1.6261... Generator Loss: 0.2618
Epoch 1/1... Batch 5950... Discriminator Loss: 0.8757... Generator Loss: 1.0411
Epoch 1/1... Batch 5960... Discriminator Loss: 1.6693... Generator Loss: 0.2550
Epoch 1/1... Batch 5970... Discriminator Loss: 1.7106... Generator Loss: 0.2487
Epoch 1/1... Batch 5980... Discriminator Loss: 1.2850... Generator Loss: 0.4918
Epoch 1/1... Batch 5990... Discriminator Loss: 1.2194... Generator Loss: 0.4798
Epoch 1/1... Batch 6000... Discriminator Loss: 1.3851... Generator Loss: 0.3683
Epoch 1/1... Batch 6010... Discriminator Loss: 1.1506... Generator Loss: 1.2183
Epoch 1/1... Batch 6020... Discriminator Loss: 1.1124... Generator Loss: 0.6335
Epoch 1/1... Batch 6030... Discriminator Loss: 1.3286... Generator Loss: 0.4439
Epoch 1/1... Batch 6040... Discriminator Loss: 1.3836... Generator Loss: 0.3506
Epoch 1/1... Batch 6050... Discriminator Loss: 1.0484... Generator Loss: 0.9458
Epoch 1/1... Batch 6060... Discriminator Loss: 1.4217... Generator Loss: 0.3460
Epoch 1/1... Batch 6070... Discriminator Loss: 2.0362... Generator Loss: 0.1754
Epoch 1/1... Batch 6080... Discriminator Loss: 1.3422... Generator Loss: 0.5079
Epoch 1/1... Batch 6090... Discriminator Loss: 2.0611... Generator Loss: 0.1621
Epoch 1/1... Batch 6100... Discriminator Loss: 1.6534... Generator Loss: 0.2700
Epoch 1/1... Batch 6110... Discriminator Loss: 1.4258... Generator Loss: 0.3686
Epoch 1/1... Batch 6120... Discriminator Loss: 1.2966... Generator Loss: 0.5969
Epoch 1/1... Batch 6130... Discriminator Loss: 1.2398... Generator Loss: 1.9787
Epoch 1/1... Batch 6140... Discriminator Loss: 1.6438... Generator Loss: 0.2700
Epoch 1/1... Batch 6150... Discriminator Loss: 1.2389... Generator Loss: 0.4467
Epoch 1/1... Batch 6160... Discriminator Loss: 0.8249... Generator Loss: 0.8902
Epoch 1/1... Batch 6170... Discriminator Loss: 1.5210... Generator Loss: 0.3018
Epoch 1/1... Batch 6180... Discriminator Loss: 1.2567... Generator Loss: 0.4438
Epoch 1/1... Batch 6190... Discriminator Loss: 0.9440... Generator Loss: 0.7487
Epoch 1/1... Batch 6200... Discriminator Loss: 1.0139... Generator Loss: 0.8238
Epoch 1/1... Batch 6210... Discriminator Loss: 1.8339... Generator Loss: 0.2480
Epoch 1/1... Batch 6220... Discriminator Loss: 1.1716... Generator Loss: 0.5570
Epoch 1/1... Batch 6230... Discriminator Loss: 1.5310... Generator Loss: 0.2914
Epoch 1/1... Batch 6240... Discriminator Loss: 0.8198... Generator Loss: 1.3734
Epoch 1/1... Batch 6250... Discriminator Loss: 2.0901... Generator Loss: 0.1874
Epoch 1/1... Batch 6260... Discriminator Loss: 1.4915... Generator Loss: 0.3159
Epoch 1/1... Batch 6270... Discriminator Loss: 1.1932... Generator Loss: 0.4305
Epoch 1/1... Batch 6280... Discriminator Loss: 0.9144... Generator Loss: 0.6410
Epoch 1/1... Batch 6290... Discriminator Loss: 1.6509... Generator Loss: 0.2610
Epoch 1/1... Batch 6300... Discriminator Loss: 1.2714... Generator Loss: 0.8549
Epoch 1/1... Batch 6310... Discriminator Loss: 0.8675... Generator Loss: 0.7639
Epoch 1/1... Batch 6320... Discriminator Loss: 1.3498... Generator Loss: 0.3860
Epoch 1/1... Batch 6330... Discriminator Loss: 1.1915... Generator Loss: 0.5185

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.